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Anomaly diagnostics and fault classification with prognostics is an active research topic, and real-time detection of anomalies and their classification has remained a critical challenge to be overcome. We developed an innovative, model-driven anomaly diagnostic and fault characterization system for electromechanical actuator (EMA) systems to mitigate catastrophic failures. The efficacy of the Model-based...
This paper studies the identification algorithm of parameters self adaptive SMO based on linear kernel function, and analyses its performance and advantages. For ARX model and long-term prediction model, the method is used to identify the model of main steam pressure of thermal system and dual-lane gas turbine engine of aero system. The simulation results show that the algorithm can effectively identify...
This paper focuses on the identification of nonlinear hybrid systems involving unknown nonlinear dynamics. The proposed method extends the framework of by introducing nonparametric models based on kernel functions in order to estimate arbitrary nonlinearities without prior knowledge. In comparison to the previous work of, which also dealt with unknown nonlinearities, the new algorithm assumes the...
This paper introduces a simple yet powerful data transformation strategy for kernel machines. Instead of adapting the parameters of the kernel function w.r.t. the given data (as in conventional methods), we adjust both the kernel hyper-parameters and the given data itself. Using this approach, the input data is transformed to be more representative of the assumptions encoded in the kernel function...
Most well-known discriminative clustering models, such as spectral clustering (SC) and maximum margin clustering (MMC), are non-Bayesian. Moreover, they merely considered to embed domain-dependent prior knowledge into data-specific kernels, while other forms of prior knowledge were seldom considered in these models. In this paper, we propose a Bayesian maximum margin clustering model (BMMC) based...
Multi-modality, the unique and important property of video data, is typically ignored in existing video adaptation processes. To solve this problem, we propose a novel approach, named multi-modality transfer based on multi- graph optimization (MMT-MGO) in this paper, which leverages multi-modality knowledge generalized by auxiliary classifiers in the source domain to assist multi-graph optimization...
Aimed at the research on freeway detection algorithm has great significance for improving efficiency and effectiveness of freeway traffic management, this paper based on the freeway traffic flow's characteristics, in accordance with the incident detection's basic principle, researches on freeway incident detection based on Support Vector Machine (SVM). This paper designs four different simulation...
The incremental updating of classifiers implies that their internal parameter values can vary according to incoming data. As a result, in order to achieve high performance, incremental learner systems should not only consider the integration of knowledge from new data, but also maintain an optimum set of parameters. In this paper, we propose an approach for performing incremental learning in an adaptive...
This paper presents a novel behavioral-level analog circuit performance modeling methodology using kernel based support vector machine (SVM). Behavioral modeling for analog circuits is in high demand for architectural exploration and system prototyping of increasingly complex electronic systems. In this paper, we investigate the effectiveness of applying SVM to model analog circuits. Based on the...
Solar radiation knowledge is important for the solar energy conversion and utilization. In this work, least squares-support vector machine (LS-SVM) algorithms were applied to estimate the yearly and monthly average daily global solar radiation in China using the ordinary meteorological data and geographic parameters. The monthly climatic data from 101 radiation measurement stations were divided into...
Grey model and support vector machine are fit for prediction in the small size of data, their advantages and disadvantages are probed in this paper at first. And then, the combined model is proposed, which combines grey model and support vector machine with optimal weights. The weights are obtained and optimized by minimizing the sum of squared residuals standard. Some experiments compared with grey...
We propose a nonsmooth bilevel programming method for training linear learning models with hyperparameters optimized via T-fold cross-validation (CV). This algorithm scales well in the sample size. The method handles loss functions with embedded maxima such as in support vector machines. Current practice constructs models over a predefined grid of hyperparameter combinations and selects the best one,...
To establish suitable models to describe the behavior of biochemistry systems, a new modeling method was introduced, combining multiple objective ant colony optimization(MOACO) with the dynamic Epsilon-SVM. The hyper-parameters of Epsilon-SVM were automatically decided by using multiple objective ant colony optimization(MOACO). Each training sample used different error. The model for penicillin production's...
Forecasting weapon system cost accurately has great meaning in determining weapons' appropriate price, reducing the cost risk and raising the efficiency of equipment expense utilization. The least squares support vector machine (LSSVM) was applied to forecast weapon system cost, and the chaos optimization algorithm, which is regarded as a good optimization method, was used to optimize the penalty...
Exploiting additional information to improve traditional inductive learning is an active research area in machine learning. In many supervised-learning applications, training data can be naturally separated into several groups, and incorporating this group information into learning may improve generalization. Recently, Vapnik proposed general approach to formalizing such problems, known as learning...
Analyzing the characteristics of wood biomass gasification process comprehensively and considering the impact of gasification temperature as well as the amount of catalyst on gasification gas components, gas yield, gas production rate, the calorific value and gasification efficiency, sawdust gasification process model based on LSSVM (least squares support vector machine) was set up. Based on this...
Methods of artificial intelligence have been widely used in the study of investment related topics, and the methods adopted include genetic algorithm and neural network, etc. However, as different to the methods taken in the past, support vector machine is adopted in this article to perform investment strategy study for domestic stock market; investment strategy can be divided into three strategies...
In SVM ensemble learning, diversity strategy is one of the most important determinants to obtain good performance. In order to examine and analyze the impacts of diversity strategies on SVM ensemble learning, this study tries to make such a deep investigation by taking credit scoring as an illustrative example. Experimental results found that the accuracy of ensemble models will be increased if ensemble...
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